Functional analysis of protein interactions using coupled bi-fluorescence complementation/GFP nanobody techniques
Bibliographic record
Abstract
Transcription factors (TFs) form homo- or hetero-dimeric DNA binding complexes along with associated co-regulators that can have transcriptional repressor or activator functions. Defining the specific composition of the complexes is therefore key to understanding their biological role. Here, we utilized bimolecular fluorescence complementation (BiFC) to visualize the formation of defined TF dimers and associated co-regulators derived from the activator protein-1 (AP-1) and myocyte enhancer factor 2 (MEF2) families. Firstly, BiFC signals were observed in cells co-expressing TFs tagged with complimentary combinations of the split fluorescent protein, demonstrating the engineered formation of defined dimer complexes. Next, we applied this approach and determined that defined AP-1 dimers localized at discrete sub-nuclear locations. Subsequently, a combination of BiFC coupled with GFP binding peptide (GBP)-nanotrap allowed observation of protein-protein interactions between a co-regulator, HDAC4, and defined BiFC-MEF2 engineered dimers. To determine transactivation properties of defined TF dimers in a cellular system, the Gal4-DNA binding domain fused to GBP was utilized to assess the transcriptional properties of the BiFC-TF dimers using a generically applicable Gal4/UAS luciferase reporter gene assay system. Here, we report efficacy of a BiFC/GBP-nanobody approach that allows engineering, visualization, and functional analysis of defined TF dimers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".